The present invention generally relates to communication signal processing, and particularly relates to using chip sample correlations in one or more received signal processing operations.
Certain types of wireless communication receivers improve their reception performance through interference suppression. For example, a “Generalized” RAKE receiver (G-RAKE) mitigates interference in a received Code Division Multiple Access (CDMA) signal by incorporating knowledge of the noise covariance into the generation of RAKE combining weights that are used to RAKE combine despread values of the received signal. While structurally different than G-RAKE receivers, chip equalization receivers similarly suppress interference by incorporating knowledge of noise covariance into their generation of filter combining weights.
The interference of interest generally comprises those interference and noise components remaining after despreading, i.e., non-orthogonal interference and noise components that appear in the despread values obtained from the received CDMA signal. Thus, conventional approaches to interference suppression measure post-despreading noise correlations, and use the post-despreading noise correlation measurements to suppress interference. For example, a conventional G-RAKE receiver estimates noise correlations from pilot symbols obtained by despreading the received CDMA signal, and then generates data signal combining weights based in part on the noise correlations estimated from the pilot symbols.
However, one shortcoming of this approach to noise correlation estimation, often carried out by constructing a “noise covariance” matrix, stems from the relatively few number of pilot symbols available over a given time interval. For example, the Wideband CDMA standards define slotted transmissions of 0.667 ms duration, during which ten pilot symbols are received. Generally these ten symbols represent an insufficiently large base from which the receiver can obtain a generally good estimate. Averaging these single-slot estimates over multiple slots can reduce estimation error. However, under some reception conditions, such as in certain fast fading environments, this multi-slot averaging window simply is too wide to track rapidly changing channel conditions.
One approach to the above tracking problem involves the use of chip sample correlations calculated from chip samples of the received signal, rather than noise correlations calculated from despread pilot symbols. This approach offers advantages in fast fading environments because new chip samples generally are available at a much higher rate than are new pilot symbols—the ratio may be as high as 256-to-1. More data points in a shorter period of time means lower estimation error, while keeping the estimation window short to allow tracking of rapidly changing conditions. However, the use of chip sample correlations instead of noise correlations as estimated from despread pilot symbols “loses” soft scaling information that generally is needed for proper combining of data despread values, chip equalization filtering, Signal-to-Interference Ratio (SIR) calculations, etc.